CAJ | 학술논문

在对集合经验模态分解( Ensemble Empirical Mode Decomposition,EEMD)进行研究之后,提出了一种利用改进的EEMD进行滚动轴承故障特征提取的方法。该方法根据EEMD的分解过程中信号和加入的白噪声的特点来选择EEMD的参数,并且对分解后所得到的的固有模态函数( IMF)分量进行阈值处理后再重构,以降低噪声的干扰。对重构后的信号进行包络谱分析,提取其故障特征,最后将该方法与通用的EEMD方法进行对比,研究结果表明EEMD是一种很有效的滚动轴承故障特征提取方法。
재대집합경험모태분해( Ensemble Empirical Mode Decomposition,EEMD)진행연구지후,제출료일충이용개진적EEMD진행곤동축승고장특정제취적방법。해방법근거EEMD적분해과정중신호화가입적백조성적특점래선택EEMD적삼수,병차대분해후소득도적적고유모태함수( IMF)분량진행역치처리후재중구,이강저조성적간우。대중구후적신호진행포락보분석,제취기고장특정,최후장해방법여통용적EEMD방법진행대비,연구결과표명EEMD시일충흔유효적곤동축승고장특정제취방법。
A method using improved EEMD to the feature extraction of rolling bearing is proposed, after re-search on Ensemble Empirical Mode Decomposition. This method select the EEMD parameters according to the characteristics of signal and the added white noise of EEMD. In order to reduce the interference of noise, through thresholding and reconstructing every intrinsic mode function . Then the envelop spectrum of the signal was analyzed, extracted the fault characteristics of the rolling bearings. Finally, comparing the method with universal EEMD method,the result shows that EEMD is a very productive method of fault diag-nosis of rolling bearings.